Machine perception and understanding of electronic information can represent a difficult task. For example, robots generally use electronic sensors such as cameras, LiDAR, and other sensors to acquire information about a surrounding environment. The information can take different forms such as still images, video, point clouds, and so on. However, understanding the contents of the information can be a complex task. In one approach, a machine learning algorithm may be employed to perform a particular perception task such as detecting obstacles within provided image data. While machine learning algorithms can be effective at such tasks, an accuracy of such approaches generally depends on the quantity, diversity, and quality of training examples that the machine learning algorithm uses to learn the task.
However, acquiring this breadth and quantity of training data can represent a significant hurdle to training the algorithm. For example, collecting actual sensor data for such a task generally involves driving a vehicle over many miles and through many different environments to collect raw data, which is then manually labeled to provide annotations in the data that can be used by the algorithm for training. As such, manually collecting and labeling sensor data is generally inefficient and often includes inaccuracies from errors introduced through the manual labeling process.
Moreover, in further approaches, a machine learning algorithm learns a perception task using simulated data such as synthetic images. When, for example, a simulator produces a synthetic image, objects, and configurations of the objects within the synthetic image are generally known or are at least easily labeled through automated processes because of the nature of the synthetic image. Accordingly, using synthetic images can avoid inefficiencies associated with the manual labeling process. However, synthetic images do not represent environments in a photo-realistic manner. As a result of this discrepancy in realism, the synthetic images can introduce a gap within the understanding of the machine learning model when used as a training source. Accordingly, difficulties with accurately training such models persist.